Selecting solution for carbon emission prediction

ABSTRACT

A computer-implemented method and non-transitory article of manufacture tangibly embodying computer readable instructions for selecting a solution for carbon emission prediction. The method includes the steps of: obtaining historical records of carbon emission and a current demand for carbon emission, locating from the historical records of carbon emission a best matching historical record with respect to the current demand, selecting, based on the located best matching historical record, one of (i) a data prediction solution record and (ii) a rule prediction solution, and calculating a demand gap between the current demand and the best matching historical record as a best matching demand gap, where at least one step is carried out using a computer device.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. §119 from ChinesePatent Application No. 201110116050.2 filed Apr. 28, 2011, the entirecontents of which are incorporated herein by reference.

BACKGROUND

1. Field of the Invention

Embodiments of the present invention generally relate to the field ofinformation technology, and more specifically, to a method, apparatus,and system for selecting a solution for carbon emission prediction.

2. Related Art

As the energy and environmental issue gets increasingly tough, carbonemission has received more and more attention. Carbon emission is ageneral term or short for greenhouse gas emission. Since excessivecarbon emissions will exert an adverse or even irreversible impact onthe environment, the control of carbon emissions is one of importantissues confronting modern production, manufacture, logistics, and otheraspects.

After a demand with respect to a specific production or transportproject is determined and before the project is put into actualoperation, in addition to predicting and evaluating the key performanceindex (KPI), it is expected to predict carbon emissions as precisely aspossible so as to adjust the plan or scheme of the protect according tothe prediction and further reduce carbon emissions for satisfying rulesand regulations, international standards, etc. In the context of thedisclosure, term “demand” refers to any carbon emissions-relatedrequirement, provision, standard or other aspect in production,manufacture, transport and other projects, including but not limited toamount of coal used, amount of power used, amount of fuel used, amountof natural gas used, storage area (e.g., warehouse area), amount ofmachine used, moving distance of machine, heating time, lighting time,and so on.

There exist several solutions for predicting carbon emission, includingbut not limited to IPCC (the Intergovernmental Panel on Climate Change)Guideline, GHG (the Greenhouse Effect) Protocol Initiative, PAS2050, andso on. Generally speaking, existing solutions for carbon emissionprediction can be divided into two types, namely data-based predictionsolutions (referred to as “data prediction solutions” for short) andnon-data-based prediction solutions (referred to as “rule predictionsolutions” for short). In a data prediction solution, a demand isquantized, and various value operations are performed on quantizedvalues to obtain a result of carbon emission prediction. By contrast, ina non-data-based prediction solution a result of carbon emissionsprediction is obtained based on a series of rules and logical judgment.It should be noted that the “non-data-based prediction solution” and“rule prediction solution” can be used interchangeably in the context ofthe disclosure.

After a project is put into practice, actual measurements of carbonemission can be measured by various means. Subsequently, a predictionprecision can be obtained by comparing the actual measurements of carbonemission with the prediction result. As known in the art, dataprediction and rule prediction can have different precisions fordifferent demands. It is to be understood that for some demands, a dataprediction solution obtains a more approximate prediction result toactual measurements than a rule prediction solution, while for otherdemands, a rule prediction solution obtains a more approximateprediction result to actual measurements than data prediction. In otherwords, precisions of these two types of solutions for carbon emissionprediction are related to demands.

SUMMARY OF THE INVENTION

One aspect of the invention includes a computer-implemented method forselecting a solution for carbon emission prediction. The method includesthe steps of: obtaining historical records of carbon emission and acurrent demand for carbon emission, locating from the historical recordsof carbon emission a best matching historical record with respect to thecurrent demand, selecting, based on the located best matching historicalrecord, one of (i) a data prediction solution record and (ii) a ruleprediction solution, and calculating a demand gap between the currentdemand and the best matching historical record as a best matching demandgap, where at least one step is carried out using a computer device

Yet another aspect of the invention includes a non-transitory article ofmanufacture tangibly embodying computer readable instructions which,when implemented, cause a computer device to carry out the steps of amethod including: obtaining historical records of carbon emission and acurrent demand for carbon emission; locating from the historical recordsof carbon emission a best matching historical record with respect to thecurrent demand; selecting, based on the located best matching historicalrecord, one of: (i) a data prediction solution record and (ii) a ruleprediction solution; and calculating a demand gap between the currentdemand and the best matching historical record as a best matching demandgap.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features, and advantages of embodiments ofthe present invention will become easier to understand from thefollowing detailed description with reference to the figures. In thefigures, several embodiments of the present invention are illustrated byway of illustration instead of limitation, where:

FIG. 1 illustrates a flowchart of a method 100 for selecting a solutionfor carbon emission prediction according to one embodiment of thepresent invention.

FIG. 2 illustrates a flowchart of a method 200 for selecting a solutionfor carbon emission prediction according to another embodiment of thepresent invention.

FIG. 3A illustrates a schematic view of a prediction precision that is afunction of a demand gap according to embodiments of the presentinvention.

FIG. 3B illustrates a schematic view of a prediction precision that is afunction of a demand gap according to embodiments of the presentinvention.

FIG. 4 illustrates a block diagram of an apparatus 401 for selecting asolution for carbon emission prediction according to one embodiment ofthe present invention.

FIG. 5 illustrates a block diagram of an apparatus 501 for selecting asolution for carbon emission prediction according to another embodimentof the present invention.

FIG. 6 illustrates a block diagram of a system 600 for selecting asolution for carbon emission prediction According to embodiments of thepresent invention.

FIG. 7 illustrates a structural block diagram of an exemplary computersystem 700 in which embodiments of the present invention can beimplemented.

DETAILED DESCRIPTION OF THE INVENTION

Although there exist two types of solutions for carbon emissionprediction as described above, in the prior art it is usually impossibleto determine in advance whether a data prediction solution or a ruleprediction solution can be used for achieving a more accurate predictionresult (i.e., more approximate to an actual measurement to be obtainedsubsequently) for a given demand. In view of this, embodiments of thepresent invention provide a method, apparatus, and system for selectinga solution for carbon emission prediction.

In an aspect of the present invention, there is provided a method forselecting a solution for carbon emission prediction. The methodincludes: obtaining historical records of carbon emission and a currentdemand for carbon emission, locating from the historical records ofcarbon emission a best matching historical record with respect to thecurrent demand, and selecting one of a data prediction solution and arule prediction solution based on the located best matching historicalrecord for carbon emission prediction for the current demand.

According to some embodiments of the present invention, a demand gapbetween the current demand and the best matching historical record iscalculated as a best matching demand gap, where the selecting one of adata prediction solution and a rule prediction solution furtherincludes: determining a data prediction precision as a function of thedemand gap, determining a rule prediction precision, and selecting oneof a data prediction solution and a rule prediction solution accordingto the best matching demand gap, the data prediction precision, and therule prediction precision.

According to some embodiments of the present invention, the determininga data prediction precision includes: determining an upper limit and alower limit of the data prediction precision. The determining a ruleprediction precision includes: determining an upper limit and a lowerlimit of the rule prediction precision. The selecting one of a dataprediction solution and a rule prediction solution includes: determininga first demand gap corresponding to the case that the lower limit of thedata prediction precision is equal to the upper limit of the ruleprediction precision, determining a second demand gap corresponding tothe case that the upper limit of the data prediction precision is equalto the lower limit of the rule prediction precision, and selecting oneof a data prediction solution and a rule prediction solution based onthe best matching demand gap, the first demand gap, and the seconddemand gap.

In particular, according to some embodiments of the present invention,the selecting one of a data prediction solution and a rule predictionsolution further includes: selecting a data prediction solutionresponsive to the best matching demand gap being less than the firstdemand gap, selecting a rule prediction solution responsive to the bestmatching demand gap being larger than the second demand gap, andresponsive to the best matching demand gap falling between the firstdemand gap and the second demand gap, selecting one of a data predictionsolution and a rule prediction solution based on an actual carbonemission measurement, a rule prediction result, and a data predictionresult associated with the best matching historical record.

According to some embodiments of the present invention, the method canfurther include: storing in the historical records the current demandand an actual carbon emission measurement for the current demand. Themethod can further include: storing the best matching demand gap, aprediction result and a prediction precision for the demand.

In another aspect of the present invention, there is provided anapparatus for selecting a solution for carbon emission prediction. Theapparatus includes: obtaining means configured to obtain historicalrecords of carbon emission and a current demand for carbon emission,record locating means configured to locate from the historical recordsof carbon emission a best matching historical record with respect to thecurrent demand, and selecting means configured to select one of a dataprediction solution and a rule prediction solution based on the locatedbest matching historical record for carbon emission prediction for thecurrent demand.

In still another aspect of the present invention, there is provided asystem for selecting a solution for carbon emission prediction. Thesystem includes: an apparatus for selecting a solution for carbonemission prediction as described above, and a carbon emission databasefor storing historical records of carbon emission, where each historicalrecord includes a demand description and an actual carbon emissionmeasurement.

With embodiments of the present invention, for any given current demand,it is possible to locate from the historical records of carbon emissiona best matching record with respect to the current demand, and furthercalculate a demand gap between the current demand and the best matchinghistorical record as a best matching demand gap. Moreover, it ispossible to calculate a data prediction precision and a rule predictionprecision of carbon emission based on existing historical records. Inparticular, prediction precisions can be functions of the demand gap.Thus, with embodiments of the present invention, it is possible todetermine which one of a data prediction solution and a rule predictionsolution is more suitable for the current demand, according torelationships between the best matching demand gap and the dataprediction precision, the rule prediction precision. In this way, anoptimal prediction solution can be selected for carbon emissionprediction.

More detailed description will be presented below to embodiments of thepresent invention by referring to the figures. As described above anddiscussed below in detail, for a given current demand, it is possible toadaptively select a data prediction solution or a rule predictionsolution for performing carbon emission prediction based on historicaldata and respective precisions of the data prediction solution and ruleprediction solution in one embodiment of the present invention.

It should be noted that the term “carbon emission” used here is ageneral term of all greenhouse gas emission. A “greenhouse gas” is a gasthat damages a normal relationship of infrared radiation between theatmosphere and the ground, absorbs infrared radiation emitted by theearth, prevents heat dissipation of the earth and gives rise toperceptible warning on the earth. Primary greenhouse gases in theearth's atmosphere include, without limitation, water vapor (H₂O),carbon dioxide (CO₂), nitrous oxide (N₂O), methane (CH₄), ozone (O₃),etc.

Reference is first made to FIG. 1, which illustrates a flowchart of amethod 100 of selecting a solution for carbon emission predictionaccording to one embodiment of the present invention. It is to beunderstood that respective steps of the method 100 can be performed indifferent orders and/or in parallel. The method 100 can further includeadditional steps and/or steps that are omitted here. The scope of thepresent invention is not limited in this regard.

After the step 100 starts, historical records of carbon emission and acurrent demand for carbon emission are obtained in step S102. Asdescribed above, in the context of the disclosure, term “demand” can beused to represent any aspect related to carbon emission, including butnot limited to amount of coal used, amount of power used, amount of fuelused, amount of natural gas used, storage area (e.g., warehouse area),moving distance of machine, using events of machine, heating time,lighting time, and so on. According to embodiments of the presentinvention, the current demand for carbon emission can be received from auser. Alternatively or additionally, the current demand for carbonemission can be obtained from other sources or generated by computation.The scope of the present invention is not limited in this regard.

Further, a historical record of carbon emission can be obtained.According to embodiments of the present invention, there existhistorical records of carbon emission, where each record (or entry) cancontain a demand with respect to a specific project and an actual carbonemission measurement of the project. As an example, Table 1 shows anexemplary structure of historical records of carbon emission. In theexample of Table 1, demands for carbon emission saved in the “demand”field are presented in the form of vectors, where each element of avector represents, for example, concrete values (whose unit is notshown) in respective specific demand aspects. In particular, if a valuein a certain demand aspect is not presented or cannot be obtained forother reason, then NULL is populated in this element. In addition,values of carbon emissions obtained by actual measurement means aresaved in the “actual measurement” field, whose unit (not shown) can beton or any proper measuring unit. It is to be understood that Table 1 ismerely exemplary, information in fields can be in any other properformat, and historical records of carbon emission can include differentor additional fields.

TABLE 1 Exemplary Structure of Historical Records Demand ActualMeasurement (20, 30.5, 1003, 200) 3.06 (30, NULL, 5202, 105) 4.51 . . .. . .

It is to be understood that the historical records of carbon emissioncan be saved in various manners, including but not limited to varioustables of databases, structured text compiled in the Extensible MarkupLanguage (XML) for example, binary or text files, etc. It is to beunderstood that information and data saved in such historical recordscan be obtained or read by presently known means or means which can bedeveloped in future. The scope of the present invention is not limitedin this regard.

Next, the method 100 proceeds to step S104 to locate from the historicalrecords of carbon emission a best matching historical record withrespect to the current demand. For a given current demand, the bestmatching historical record can be located from historical records invarious manners. For example, according to some embodiments of thepresent invention, it is possible to treat a demand as a vector andtreat the demand's respective concrete aspects (e.g., amount of coalused, amount of power used, etc.) of the demand as the vector'selements. In this way, a distance between demands can be calculated bycalculating a distance (e.g., Euclidean distance) between vectors. Ahistorical record corresponding to a historical demand having theshortest distance from the current demand can be selected as the bestmatching historical record.

In particular, in some embodiments of the present invention, whencalculating a distance between vectors, it is possible to assigncorresponding weights to some elements according to actual applicationand conditions. For example, a project associated with the currentdemand attaches special attention to the amount of coal used, a weightof the amount of coal used can be increased during the distancecalculation.

The above-described locating of the best matching historical recordthrough a distance between vectors is merely exemplary. In fact, thoseskilled in the art can contemplate other appropriate ways for locatingthe best matching historical record with respect to the current demand.For example, it is possible to perform a quantitative operation on ormake a qualitative comparison (e.g., comparison with a given threshold)of respective indices in the demand. Alternatively, the best matchinghistorical record can be manually specified. The scope of the presentinvention is not limited in this regard.

Subsequently in step S106, based on the best matching historical recordlocated in step S104, a data prediction solution or a rule predictionsolution is selected for carbon emission prediction with respect to thecurrent demand. Step S106 can be performed in various manners. Forexample, in some embodiments, it is possible to determine respectiveprecisions of a data prediction solution and a rule prediction solutionwith respect to the demand of the best matching historical record.Specifically, it is possible to respectively apply a data predictionsolution and a rule prediction solution to the demand of the bestmatching historical record located in step S104, so as to obtain a dataprediction result and a rule prediction result. Then, a data predictionprecision and a rule prediction precision (e.g., in the form of apercentage) can be obtained by comparing the data prediction result andthe rule prediction result with the actual carbon emission measurementstored in the historical record, respectively.

It is to be understood that such data prediction precision and ruleprediction precision can be dynamically calculated in runtime, orcalculated and stored in advance. In any event, for each historicalrecord of carbon emission, when a corresponding actual carbon emissionmeasurement corresponding is obtained, a data prediction precision and arule prediction precision can be calculated as described above. Then,the calculated data prediction precision and rule prediction precisioncan be stored separately, for example, in a data prediction knowledgerepository and a rule prediction knowledge repository (to be describedbelow in detail), respectively. Like the historical records of carbonemission, the data prediction knowledge repository and rule predictionknowledge repository can be implemented in various manners as describedabove. Further, according to embodiments of the present invention, thehistorical records, data prediction knowledge repository, and ruleprediction knowledge repository can be implemented either separately orin conjunction with one another. The scope of the present invention isnot limited in these regards.

It is further to be understood that according to embodiments of thepresent invention, there can exist various data prediction solutionsand/or rule prediction solutions which are presently known or will bedeveloped in future. It is possible to select a solution with thehighest prediction precision from a plurality of data/rule predictionsolutions. Other selection polices are possible as well. For example, insome other embodiments, the selection of a concrete data/rule predictionsolution can depend on specific needs and application or be specifiedmanually. The scope of the present invention is not limited in thisregard.

The method 100 ends after step S106. By the method 100, it is possibleto find a historical record having the smallest gap with the currentdemand, namely the best matching historical record. Further, it ispossible to select one of a data prediction solution and a ruleprediction solution based on their prediction precisions with respect tothe best matching historical record for carbon emission prediction forthe current demand.

Reference is now made to FIG. 2, which illustrates a flowchart of amethod 200 of selecting a solution for carbon emission predictionaccording to another embodiment of the present invention. The method 200can be implemented as a preferred embodiment of the method 100 describedabove with reference to FIG. 1. It will be apparent from the followingdescription that according to the method 200, after the best matchinghistorical record with respect to the current demand is located, aquantized demand gap therebetween can be calculated and a dataprediction precision and a rule prediction precision are treated asfunctions of the demand gap. In this way, an optimal solution for carbonemission prediction can be selected by quantizing the demand gap.

After the method 200 starts, historical records and a current demand forcarbon emission are obtained in step S202; the best matching historicalrecord with respect to the current demand is located from the historicalrecords of carbon emission in step S204. Steps S202 and S204 correspondto steps S102 and S104 described with reference to FIG. 1, respectively,and thus are not detailed here.

Next, the method 200 proceeds to step S206 where a demand gap betweenthe best matching historical record located in step S204 and the currentdemand is calculated. As described above with reference to step S104 ofthe method 100, the demand gap can be calculated in various manners. Ina preferred embodiment of the present invention, for example, the demandgap can be calculated by using a vector distance. Of course, otherembodiments are also feasible.

It is to be understood that as described above, the best matchinghistorical record can be determined by calculating a demand gap betweenthe current demand and each historical record in step S204. At thispoint, the operation of step S206 has been actually accomplished in stepS204. In this case, after the demand gap is calculated and used forlocating the best matching historical record, it is no more discardedbut recorded and retained for subsequent use (which differs from themethod 100).

The method 200 then proceeds to step S208 where a data predictionprecision of carbon emission is calculated, the data predictionprecision being a function of the demand gap. According to embodimentsof the present invention, the calculation of the data predictionprecision can be based on the above-mentioned data prediction knowledgerepository. According to embodiments of the present invention, eachentry in the data prediction knowledge repository corresponds to oneentry in the historical records of carbon emission, and can include a“data prediction result” field and a “best matching demand gap” field.

For any given entry in the data prediction knowledge repository, the“data prediction result” field can store a result of carbon emissionprediction obtained by the data prediction solution, and the “bestmatching demand gap” field can store a demand gap (i.e., best matchingdemand gap) between a demand corresponding to the entry and the bestmatching historical record among the historical records of carbonemission. Here, the “best matching historical record” and “best matchingdemand gap” can be determined in a similar manner as described abovewith reference to step S204 and S206.

Further, the entry in the data prediction knowledge repository caninclude a “data prediction precision” field. As described above, suchkind of data prediction precision can be calculated and stored when anactual carbon emission measurement can be obtained. It is to beunderstood that the “data prediction precision” field is optional. Infact, when a data prediction precision is needed, it can be calculatedin real time according to an actual measurement stored in the historicalrecord and a value stored in the “data prediction result” field. Table 2shows an exemplary structure of the data prediction knowledgerepository. It should be noted that in the example shown in Table 2, the“best matching demand gap” field saves a value of a Euclidean distancebetween two demand vectors. However, this is merely exemplary. The bestmatching demand gap can have other proper formats, depending on concreteapplication and calculation method.

TABLE 2 Exemplary Structure of Data Prediction Knowledge Repository DataBest Matching Demand Data Prediction Precision Prediction Result Gap(Optional) 25.30 3.00 89% 1003.74 1.72 73% . . . . . . . . .

In step S208, based on one or more entries in the data predictionknowledge repository, a data prediction precision can be calculated as afunction of the demand gap in various ways. As an example, reference ismade to FIG. 3A, which illustrates a schematic view of a predictionprecision being a function of the demand gap according to someembodiments of the present invention. In the embodiment illustrated inFIG. 3A, the horizontal axis represents gaps between demands, and thelongitudinal axis represents prediction precisions. Since each entry inthe knowledge repository stores the best matching demand gap and a dataprediction precision (or a data prediction precision can be calculated),each entry in the knowledge repository can corresponds to a point in thecoordinate plane.

Thus in the embodiment illustrated in FIG. 3A, one or more points in thecoordinate plane can be obtained. By fitting these points, the resultingcurve can be used as a data prediction precision D. It is to beunderstood that any curve fitting method or algorithm that is presentlyknown or will be developed in future can be used in the embodiment ofthe present invention, so as to generate the data prediction precisionD; the scope of the present invention is not limited in this regard.Obviously the data prediction precision D is a function of the demandgap in the embodiment illustrated in FIG. 3A.

The method 200 subsequently proceeds to step S210 where a ruleprediction precision of carbon emission is calculated. According toembodiments of the present invention, the calculation of the ruleprediction precision can be based on a rule prediction knowledgerepository. Each entry in the rule knowledge repository can include“rule prediction result” field for storing a result of carbon emissionprediction obtained by the rule prediction solution. Further, the entryin the rule prediction knowledge repository can include a “ruleprediction precision” field. As described above, such kind of ruleprediction precision can be calculated and stored when an actual carbonemission measurement can be obtained. It is to be understood that the“rule prediction precision” field is also optional, just like the “dataprediction precision” field in the data prediction knowledge repository.

TABLE 3 Exemplary Structure of Rule Prediction Knowledge Repository RulePrediction Result Rule Prediction Precision (Optinoal)  25.72 92% 987.5983% . . . . . .

It should be noted that according to some embodiments of the presentinvention, the entry in the rule prediction knowledge repository canexclude the “best matching demand gap” field, which is determined byinherent characteristics of the rule prediction solution. Specifically,the rule prediction solution can determine or learn the relationshipbetween an inputted demand and carbon emission primarily based onrelevant demand and empirical rule. Therefore the rule predictionprecision does not vary with the change of demand gap as significantlyas the data prediction precision. The “best matching demand gap” fieldcan thus be omitted in the rule prediction knowledge repository. Ofcourse, this is not limiting, and those skilled in the art canunderstand that the entry in the rule prediction knowledge repositorycan also have the “best matching demand gap” field.

In step S210, based on one or more entries in the rule predictionknowledge repository, a rule prediction precision can be calculated invarious ways. For example, a rule prediction precision R can be theaverage, weighted average, or maximum of all prediction precisions inthe rule prediction knowledge repository. Other manners for calculatingthe rule precision R are also feasible, and the scope of the presentinvention is not limited in this regard. Still referring to theembodiment illustrated in FIG. 3A, it can be seen that in this examplethe rule prediction precision R can be understood as a constant functionof the demand gap. In other words, the rule prediction precision R doesnot vary with the change of demand gap in this example.

As described above, the rule prediction precision R does not vary withthe change of demand gap as significantly as the data predictionprecision. Hence, calculating the rule prediction precision R as aconstant function of the demand gap is sufficient for the object of thepresent invention. However, this is not limiting, and those skilled inthe art can understand that just like the data prediction precision D,the rule prediction precision R can also be calculated as a non-constantfunction of the demand gap.

The method 200 then proceeds to step S212 where a data predictionsolution or a rule prediction solution is selected based on the bestmatching demand gap, data prediction precision, and rule predictionprecision determined in step S206, S208, and S210, respectively.Specifically, it can be determined in step S212 which one of the dataprediction precision and the rule prediction precision is higher at thebest matching demand gap associated with the current demand.

Refer to FIG. 3A again. In this example, suppose the best matchingdemand gap associated with the current demand is G. At this point,determination as to which one of the data prediction precision solutionand the rule prediction precision solution is higher is made based onthe intersection of a straight line, which is perpendicular to thehorizontal axis and corresponding to the best matching demand gap G,with the data prediction precision D and the rule prediction precisionR. Then, a prediction solution having a higher prediction precision atthe best matching demand gap G is selected for carbon emissionprediction with respect to the current demand.

It is to be understood that although steps S208 to S212 of the method200 have been described with reference to FIG. 3A, this is merelyexemplary. In fact, these steps of the method 200 can be put intopractice in any other proper manner. By way of example, referring toFIG. 3B, a schematic view of a prediction precision as a function of thedemand gap according to some embodiments of the present invention isillustrated. Like FIG. 3A, in the embodiment illustrated in FIG. 3B, thehorizontal axis represents gaps between demands, and the longitudinalaxis represents data prediction precisions and rule predictionprecisions.

Unlike FIG. 3A, in the embodiment illustrated in FIG. 3B, thedetermining a data prediction precision in step S208 includesdetermining an upper limit and a lower limit of the data predictionprecision, so as to make a more accurate comparison between a dataprediction precision and a rule prediction precision. In other words,unlike curve-fitting respective points to obtain a single dataprediction precision in FIG. 3A, points distributed in the coordinateplane are considered as a plane point set in the example of FIG. 3B.Then, a plane bounding box of the point set is calculated to obtain anupper limit and a lower limit of the point set, namely the upper limitD1 and the lower limit D2 of the data prediction precision. Obviously,the upper and lower limits of the data prediction precision determinedin this way are also functions of the demand gap.

Further, in the embodiment illustrated in FIG. 3B, determining a ruleprediction precision in step S210 includes determining an upper limitand a lower limit of the rule prediction precision. For example, basedon the entry in the rule prediction knowledge repository, the highestprecision and the lowest precision of rule prediction can be determined,and they are considered as an upper limit R1 and a lower limit R2 of therule prediction precision, respectively. Like FIG. 3A, the upper limitR1 and the lower limit R2 of the rule prediction precision are constantfunctions of the demand gap.

In the embodiment illustrated in FIG. 3B, the selecting a dataprediction solution or a rule prediction solution in step S212 can beimplemented by the following sub-steps. First of all, a first demand gapG1 is determined which corresponds to the case that the lower limit D2of the rule prediction precision is equal to the upper limit R1 of therule prediction precision. This can be done by projecting theintersection point of D2 and R1 to the horizontal axis. Next, a seconddemand gap G2 is similarly calculated which corresponds to the case thatthe upper limit D1 of the data prediction precision is equal to thelower limit R2 of the rule prediction precision. Finally, the dataprediction or rule prediction can be selected based on a relationshipamong the best matching demand gap G with the current user demand asdetermined in step S206, the first demand gap G1, and the second demandgap G2.

In particular, according to some embodiments of the present invention,responsive to the best matching demand gap G being less than the firstdemand gap G1, a data prediction solution is selected for the currentdemand, because historical records indicate that in this case the upperlimit of the rule prediction precision is still less than the lowerlimit of the data prediction precision. Similarly, responsive to thebest matching demand gap G being larger than the first demand gap G1, arule prediction solution is selected for the current demand, becausehistorical records indicate that in this case the upper limit of thedata prediction precision is still less than the lower limit of the ruleprediction precision.

Responsive to the best matching demand gap G falling between G1 and G2,according to some embodiment of the present invention, a data predictionsolution or a rule prediction solution can be selected according to theactual carbon emission measurement, the rule prediction result, and thedata prediction result with respect to the best matching historicalrecord (located in step S204). For example, the selection can be made bycomparing which one of the data prediction result and the ruleprediction result is more approximate to the actual carbon emissionmeasurement. In other words, with respect to the best matchinghistorical record, if the difference between the data prediction resultand the actual carbon emission measurement is less than the differencebetween the rule prediction result and the actual carbon emissionmeasurement, then a data prediction solution is selected for carbonemission prediction with respect to the current demand; otherwise, arule prediction solution is selected. It is to be understood that thisis merely exemplary, any other proper selection mechanisms are feasible,and the scope of the present invention is not limited in this regard.

It is to be understood that what has been described above with referenceto FIGS. 3A and 3B is merely examples of selecting a data predictionsolution or a rule prediction solution according to a demand gap, a dataprediction precision, and a rule prediction precision, and is notintended to limit the scope of the present invention. Based on theteaching and suggestion offered here, those skilled in the art canreadily contemplate other methods of making selection based on thesefactors.

Returning to FIG. 2, the method proceeds to step S214 where the currentdemand and an actual carbon emission measurement for the current demandare stored in the historical records. Step S214 is optional, with apurpose of using information related to the current demand to furtherfill and perfect the historical records and thus providing a morereliable prediction basis for a subsequent demand. This is essentially alearning procedure of historical records. It is to be understood thatthe actual carbon emission measurement for the current demand is usuallyobtained after the project is put into practice. Accordingly, in stepS214 first information related to the current demand can be populated tothe records, and subsequently an actual measurement is populated when itis available.

Then the method 200 proceeds to step S216 to store the best matchingdemand gap determined in step S206, the prediction result and theprediction precision for the current demand. It is to be understood thatbased on a data prediction solution or a rule prediction solution beingselected in step S212, corresponding information will stored in stepS216 at different locations. Specifically, when a data predictionsolution is selected in step S212, the best matching demand gap, theprediction result and the prediction precision obtained by dataprediction can be stored, for example, in the above-described dataprediction knowledge repository in step S216. Similarly, when a ruleprediction solution is selected in step S212, the relevant informationcan be stored in the rule prediction knowledge repository.

It is to be understood that like step S214, step S216 is optional, whichserves a purpose of using information related to the current demand tofurther fill and perfect the data prediction knowledge repository or therule prediction knowledge repository and thus achieving a learningprocedure. Furthermore, it is to be understood that in step S214 theprediction precision is calculated and stored after the actual carbonemission measurement is available.

The method 200 ends after step S216. According to the method 200, therelationships between the data prediction precision, the rule predictionprecision and the demand gap are brought to full use, thereby making itpossible to select an optimal prediction solution for the current demandbased on historical records.

Referring to FIG. 4, a block diagram of an apparatus 401 for selecting asolution for carbon emission prediction according to one embodiment ofthe present invention is illustrated. As illustrated in FIG. 4,According to embodiments of the present invention, the apparatus 401 caninclude: obtaining means 402 configured to obtain historical records ofcarbon emission and a current demand for carbon emission; recordlocating means 404 configured to locate from the historical records ofcarbon emission a best matching historical record with respect to thecurrent demand; and selecting means 406 configured to select one of adata prediction solution and a rule prediction solution based on thelocated best matching historical record for carbon emission predictionfor the current demand.

Further referring to FIG. 5, a block diagram of an apparatus 501 forselecting a solution for carbon emission prediction according to apreferred embodiment of the present invention is illustrated. Theapparatus 501 illustrated in FIG. 5 can be implemented as a preferredembodiment of the apparatus 401 described above with reference to FIG.4. As illustrated in FIG. 5, According to embodiments of the presentinvention, the apparatus 501 can include: obtaining means 502, recordlocating means 504, and selecting means 506, which correspond to theobtaining means 402, the record locating means 404, and the selectingmeans 404 described with reference to FIG. 4, respectively, andtherefore are not detailed here.

According to some embodiments of the present invention, the apparatus501 can further include demand gap calculating means 508 configured tocalculate a demand gap between the current demand and the best matchinghistorical record as a best matching demand gap. At this point, theselecting means 506 can further include (not shown in the figure): dataprediction precision determining means configured to determine a dataprediction precision as a function of the demand gap; a rule predictionprecision determining means configured to determine a rule predictionprecision, and first selecting means configured to select one of a dataprediction solution and a rule prediction solution according to the bestmatching demand gap, the data prediction precision, and the ruleprediction precision.

In particular, according to some embodiments of the present invention,the data prediction precision determining means can further includemeans configured to determine an upper limit and a lower limit of thedata prediction precision, and the rule prediction precision determiningmeans can further include means configured to determine an upper limitand a lower limit of the rule prediction precision. In such embodiments,the first selecting means further includes: first demand gap determiningmeans configured to determine a first demand gap corresponding to thecase that the lower limit of the data prediction precision is equal tothe upper limit of the rule prediction precision, second demand gapdetermining means configured to determine a second demand gapcorresponding to the case that the upper limit of the data predictionprecision is equal to the lower limit of the rule prediction precision,and second selecting means configured to select one of a data predictionsolution and a rule prediction solution based on the best matchingdemand gap, the first demand gap, and the second demand gap.

According to some embodiments of the present invention, the secondselecting means further includes: means configured to select a dataprediction solution responsive to the best matching demand gap beingless than the first demand gap, means configured to select a ruleprediction solution responsive to the best matching demand gap beinglarger than the second demand gap, and/or means configured to select,responsive to the best matching demand gap falling between the firstdemand gap and the second demand gap, one of a data prediction solutionand a rule prediction solution based on an actual carbon emissionmeasurement, a rule prediction result, and a data prediction resultassociated with the best matching historical record.

In addition, according to some embodiments of the present invention, theapparatus 501 can further include first storing means 510 configured tostore in the historical records the current demand and an actual carbonemission measurement for the current demand. The apparatus 501 canfurther include second storing means 512 configured to store the bestmatching demand gap, a prediction result and a prediction precision forthe demand.

It is to be understood that all means and sub-means in the apparatus 401and the apparatus 501 described above with reference to FIGS. 4 and 5correspond to respective steps of the method 100 and the method 200described with reference to FIGS. 1 and 2, respectively. Hence, allabove-described features and operations of the method 100 and the method200 are also suitable for the apparatus 401 and the apparatus 501, whichare not detailed here.

It is further to be understood that the apparatus 401 and the apparatus501 can be implemented in various manners, including software, hardware,firmware, or an arbitrary combination thereof. For example, in someembodiments, the apparatus 401 and the apparatus 501 can be implementedusing software and/or firmware modules. Alternatively or additionally,the apparatus 401 and the apparatus 501 each can be implemented usinghardware modules. For example, the apparatus 401 and the apparatus 501each can be implemented as an Integrated Circuit (IC) chip or anApplication-Specific Integrated Circuit (ASIC). Also, apparatus 401 andthe apparatus 501 each can be implemented as a System On-Chip (SOC).Other manners, whether currently known or developed in future, arepossible as well. The scope of the present invention is not limited inthis regard.

Referring to FIG. 6, a block diagram of a system 600 for selecting asolution for carbon emission prediction according to one embodiment ofthe present invention is illustrated. As shown in FIG. 6, According toembodiments of the present invention, the system 600 includes: anapparatus 601 for selecting a solution for carbon emission prediction,which corresponds to the apparatus 401 described with reference to FIG.4 or the apparatus 501 described with reference to FIG. 5 in terms ofstructure and function, and a carbon emission database 602 for storinghistorical records of carbon emission, where each historical recordincludes a demand description and an actual carbon emission measurement.

According to some embodiments of the present invention, the system canfurther include: a data prediction knowledge repository 604 configuredto store a prediction result obtained by a data prediction solution, ademand gap with the best matching historical record stored in the carbonemission database, and a data prediction precision, and a ruleprediction knowledge repository 606 configured to store a predictionresult obtained by a rule prediction solution and a rule predictionprecision.

According to some embodiments of the present invention, the system 600can further include: a data prediction engine 608 configured to performcarbon emission prediction for the current demand by using a dataprediction solution that is selected by the apparatus for selecting asolution for carbon emission prediction, and a rule prediction engine610 configured to perform carbon emission prediction for the currentdemand by using a rule prediction solution that is selected by theapparatus for selecting a solution for carbon emission prediction.

FIG. 7 schematically illustrates a structural block diagram of anexemplary computer system 700 in which embodiments according to thepresent invention can be implemented. As illustrated, the computersystem 700 can include: a CPU (central processing unit) 701, RAM (randomaccess memory) 702, ROM (read only memory) 703, a system bus 704, a harddisk controller 705, a keyboard controller 706, a serial interfacecontroller 707, a parallel interface controller 708, a displaycontroller 709, a hard disk 710, a keyboard 711, a serial externaldevice 712, a parallel external device 713 and a display 714.

Among these components, the CPU 701, the RAM 702, the ROM 703, the harddisk controller 705, the keyboard controller 706, the serial interfacecontroller 707, the parallel interface controller 708, and the displaycontroller 709 are connected to the system bus 704, the hard disk 710 isconnected to the hard disk controller 705, the keyboard 711 is connectedto the keyboard controller 706, the serial external device 712 isconnected to the serial interface controller 707, the parallel externaldevice 713 is connected to the serial interface controller 708, and thedisplay 714 is connected to the display controller 709. It is to beunderstood that the structural block diagram illustrated in FIG. 7 ismerely illustrative rather than limiting the scope of the presentinvention. Some devices can be added or omitted according tocircumstances.

Particularly, in addition to a hardware implementation, embodiments ofthe present invention can be implemented as a computer program product.For example, both the method 100 described with reference to FIG. 1 andthe method 200 described with reference to FIG. 2 can be implemented asa computer program product. The computer program product can be storedin the RAM 702, the ROM 703, the hard disk 710 as illustrated in FIG. 7and/or any proper memory medium, or be downloaded to the computer system700 from a proper location via a network.

The computer program product can include a computer code portion, whichincludes program instructions executable by a proper processingapparatus (e.g., the CPU 701 illustrated in FIG. 7). The programinstructions can at least include: program instructions for obtaininghistorical records of carbon emission and a current demand for carbonemission, program instructions for locating from the historical recordsa best matching historical record with respect to the current demand,and program instructions for selecting one of a data prediction solutionand a rule prediction solution based on the located best matchinghistorical record for carbon emission prediction for the current demand.

The ideas and principles of the present invention have been explained bymeans of several embodiments of the present invention. As is clear fromthe foregoing description, it is possible to adaptively select a dataprediction solution or a rule prediction solution for carbon emissionprediction for a given demand, based on a demand gap between the demandand a historical record.

It should be noted that each block in the foregoing flowcharts or blockdiagrams can represent a module, a program segment, or a part of code,which contains one or more executable instructions for performingspecified logic functions. It should further be noted that in somealternative implementations, functions indicated in blocks can occur inan order differing from the order as shown in the figures. For example,two blocks shown consecutively can be performed in parallelsubstantially or in an inverse order sometimes, which depends on thefunctions involved. In addition, it should be noted that each block anda combination of blocks in the block diagrams or flowcharts can beimplemented by a dedicated, hardware-based system for performingspecified functions or operations or by a combination of dedicatedhardware and computer instructions.

The methods and apparatuses according to embodiments of the presentinvention can be implemented in full hardware, full software, orcombination of hardware components and software components. In apreferred embodiment, the present invention is implemented as software,including, without limitation to, firmware, resident software,micro-code, etc.

Moreover, the present invention can be implemented as a computer programproduct used by computers or accessible by computer-readable media thatprovide program code for use by or in connection with a computer or anyinstruction executing system. For the purpose of description, acomputer-usable or computer-readable medium can be any tangible meansthat can contain, store, communicate, propagate, or transport theprogram for use by or in connection with an instruction executionsystem, apparatus, or device.

The medium can be an electric, magnetic, optional, electromagnetic,infrared, or semiconductor system (apparatus or device), or propagationmedium. Examples of the computer-readable medium can include thefollowing: a semiconductor or solid memory device, a magnetic tape, aportable computer diskette, a random access memory (RAM), a read-onlymemory (ROM), a hard disk, and an optical disk. Examples of the currentoptical disk include a compact disk read-only memory (CD-ROM), compactdisk-read/write (CR-ROM), and DVD.

A data processing system adapted for storing and/or executing programcode can include at least one processor that is coupled to a memoryelement directly or via a system bus. The memory element can include alocal memory usable during actually executing the program code, a massmemory, and a cache that provides temporary memory for at least oneportion of program code so as to decrease the number of times forretrieving code from the mass memory during execution.

An Input/Output or I/O device (including, without limitation to, akeyboard, a display, a pointing device, etc.) can be coupled to thesystem directly or via an intermediate I/O controller.

A network adapter can also be coupled to the system such that the dataprocessing system can be coupled to other data processing systems,remote printers or memory devices via an intermediate private or publicnetwork. A modem, a cable modem, and an Ethernet card are merelyexamples of a currently available network adapter.

Although several embodiments of the present invention have beendescribed above, those skilled in the art can appreciate that thedescription is merely illustrative and exemplary. Under the teaching ofthis specification, various modifications and alterations can be made toembodiments of the present invention without departing from the truespirit of the present invention. Therefore, features disclosed in thespecification should not be deemed as limiting. The scope of the presentinvention is defined by the appended claims only.

What is claimed is:
 1. A computer-implemented method for selecting asolution for carbon emission prediction comprising the steps of:obtaining historical records of carbon emission and a current demand forcarbon emission; locating from the historical records of carbon emissiona best matching historical record with respect to the current demand;selecting, based on the located best matching historical record, one of:(i) a data prediction solution record and (ii) a rule predictionsolution; and calculating a demand a between the current demand and thebest matching historical record as a best matching demand gap; whereinat least one step is carried out using a computer device.
 2. The methodaccording to claim 1, wherein the selecting step further comprises thesteps of: determining a data prediction precision as a function of thedemand gap; determining a rule prediction precision; and selecting oneof: (i) a data prediction solution and (ii) a rule prediction solutionaccording to the best matching demand gap, the data predictionprecision, and the rule prediction precision.
 3. The method according toclaim 2, wherein the determining a data prediction step furthercomprises the step of: determining an upper limit and a lower limit ofthe data prediction precision, wherein the determining a rule predictionprecision step further comprises the step of: determining an upper limitand a lower limit of the rule prediction precision, and wherein theselecting according to the best matching demand gap, the data predictionprecision, and the rule prediction precision step further comprises thesteps of: determining a first demand gap corresponding to a case thatthe lower limit of the data prediction precision is equal to the upperlimit of the rule prediction precision; determining a second demand gapcorresponding to a case that the upper limit of the data predictionprecision is equal to the lower limit of the rule prediction precision;and selecting, based on the best matching demand gap, the first demandgap, and the second demand gap, one of: (i) a data prediction solutionand (ii) a rule prediction solution.
 4. The method according to claim 3,wherein the selecting, based on the best matching demand gap, the firstdemand gap, and the second demand gap step further comprises the stepof: selecting a data prediction solution responsive to the best matchingdemand gap that is less than the first demand gap.
 5. The methodaccording to claim 3, wherein the selecting, based on the best matchingdemand gap, the first demand gap, and the second demand gap step furthercomprises the step of: selecting a rule prediction solution responsiveto the best matching demand gap that is larger than the second demandgap.
 6. The method according to claim 3, wherein the selecting, based onthe best matching demand gap, the first demand gap, and the seconddemand gap step further comprises the step of: selecting, based on anactual carbon emission measurement, a rule prediction result, and a dataprediction result associated with the best matching historical record,and in response to the best matching demand gap falling between thefirst demand gap and the second demand gap, one of: (i) a dataprediction solution and (ii) a rule prediction solution.
 7. The methodaccording to claim 1 further comprising the step of: storing, in thehistorical records, the (i) current demand and (ii) an actual carbonemission measurement for the current demand.
 8. The method according toclaim 1, further comprising the step of: storing the best matchingdemand gap, a prediction result and a prediction precision for thedemand.
 9. A non-transitory article of manufacture tangibly embodyingcomputer readable instructions which, when implemented, cause a computerdevice to carry out the steps of a method comprising: obtaininghistorical records of carbon emission and a current demand for carbonemission; locating from the historical records of carbon emission a bestmatching historical record with respect to the current demand;selecting, based on the located best matching historical record, one of:(i) a data prediction solution record and (ii) a rule predictionsolution; and calculating a demand gap between the current demand andthe best matching historical record as a best matching demand gap.